Rancangan Model Dimensi Kimball Pada Data Warehouse Perusahaan Manufaktur
DOI:
https://doi.org/10.33633/joins.v11i1.14670Kata Kunci:
Data Warehouse, Model Dimensional, Kimball, Skema Bintang, ManufakturAbstrak
Perusahaan manufaktur modern menghadapi tantangan signifikan dari volume data yang besar dan sistem informasi yang terfragmentasi, yang menghambat pengambilan keputusan berbasis data yang efektif. Penelitian ini bertujuan untuk mengatasi masalah ini dengan merancang model dimensional untuk Data warehouse di perusahaan manufaktur elektronik, mengintegrasikan data operasional yang tersebar ke dalam satu repositori terpadu. Dengan menerapkan metodologi Kimball's Business Dimensional Life Cycle, penelitian ini secara sistematis melalui empat tahap: menentukan proses bisnis inti, mendeklarasikan granularitas data, mengidentifikasi dimensi, dan mengidentifikasi fakta. Hasilnya adalah sebuah model konstelasi fakta (skema bintang) yang terdiri dari serangkaian skema bintang untuk setiap proses bisnis utama, termasuk pembelian material, produksi, penjualan, dan retur barang. Model ini menghubungkan tabel-tabel fakta seperti Pembelian Material dan Produksi Barang dengan tabel dimensi yang relevan seperti Pemasok, Material, dan Pelanggan. Model yang diusulkan ini menyederhanakan akses data untuk analisis mendalam, menyediakan kerangka kerja yang kuat dan dapat digunakan kembali untuk mendukung pengambilan keputusan strategis di lingkungan manufaktur modern.Referensi
J. Xie, L. Sun, and Y. F. Zhao, “On the Data Quality and Imbalance in Machine Learning-based Design and Manufacturing—A Systematic Review,” Engineering, vol. 45, pp. 105–131, Feb. 2025, doi: 10.1016/j.eng.2024.04.024.
S. Ponnusamy, “Evolution of Enterprise Data warehouse: Past Trends and Future Prospects,” International Journal of Computer Trends and Technology, vol. 71, no. 9, pp. 1–6, Sep. 2023, doi: 10.14445/22312803/IJCTT-V71I9P101.
O. Serradilla, E. Zugasti, J. Rodriguez, and U. Zurutuza, “Deep Learning Models for Predictive Maintenance: a Survey, Comparison, Challenges and Prospects,” Applied Intelligence, vol. 52, no. 10, pp. 10934–10964, Aug. 2022, doi: 10.1007/s10489-021-03004-y.
M. Fahmideh and G. Beydoun, “Big Data Analytics Architecture Design — An Application in Manufacturing Systems,” Comput. Ind. Eng., vol. 128, pp. 948–963, Feb. 2019, doi: 10.1016/j.cie.2018.08.004.
P. R. Agustiana, Wilson, and J. S. Suroso, “Data Quality Risk Management in the Data Quality Issue Management System at Private Banking Using the OCTAVE Allegro Approach,” Buletin Poltanesa, vol. 26, no. 1, Jun. 2025, doi: 10.51967/tanesa.v26i1.3312.
A. Cakir, Ö. Akın, H. F. Deniz, and A. Yılmaz, “Enabling Real Time Big Data Solutions for Manufacturing at Scale,” J. Big Data, vol. 9, no. 1, p. 118, Dec. 2022, doi: 10.1186/s40537-022-00672-6.
C. T. Gonçalves, M. J. A. Gonçalves, and M. I. Campante, “Developing Integrated Performance Dashboards Visualisations Using Power BI as a Platform,” Information, vol. 14, no. 11, p. 614, Nov. 2023, doi: 10.3390/info14110614.
S. Ponnusamy, “Evolution of Enterprise Data warehouse: Past Trends and Future Prospects,” International Journal of Computer Trends and Technology, vol. 71, no. 9, pp. 1–6, Sep. 2023, doi: 10.14445/22312803/IJCTT-V71I9P101.
A. Al-Okaily, M. Al-Okaily, A. P. Teoh, and M. M. Al-Debei, “An Empirical Study on Data warehouse Systems Effectiveness: the Case of Jordanian Banks in The Business Intelligence Era,” EuroMed Journal of Business, vol. 18, no. 4, pp. 489–510, Oct. 2023, doi: 10.1108/EMJB-01-2022-0011.
K. Ragazou, I. Passas, A. Garefalakis, and C. Zopounidis, “Business Intelligence Model Empowering SMEs to Make Better Decisions and Enhance Their Competitive Advantage,” Discover Analytics, vol. 1, no. 1, p. 2, Feb. 2023, doi: 10.1007/s44257-022-00002-3.
M. Krajčovič, V. Bastiuchenko, B. Furmannová, M. Botka, and D. Komačka, “New Approach to the Analysis of Manufacturing Processes with the Support of Data Science,” Processes, vol. 12, no. 3, p. 449, Feb. 2024, doi: 10.3390/pr12030449.
A. R. Quitaleg and M. G. Ortiz, “Design and Development of Data warehouse Framework of Highland Vegetable Crops for Benguet,” IOP Conf. Ser. Mater. Sci. Eng., vol. 803, no. 1, p. 012035, Apr. 2020, doi: 10.1088/1757-899X/803/1/012035.
M. Hasan, Z. Ghinafikar, and M. A. Yaqin, “Perancangan Data warehouse untuk Perusahaan OTOBIS,” Jurnal Manajemen Teknologi Informatika, vol. 2, no. 3, pp. 535–546, Dec. 2024, doi: 10.70038/jentik.v2i3.125.
S. Bimonte, E. Gallinucci, P. Marcel, and S. Rizzi, “Logical design of multi-model data warehouses,” Knowl. Inf. Syst., vol. 65, no. 3, pp. 1067–1103, Mar. 2023, doi: 10.1007/s10115-022-01788-0.
T. A. Abdel-Aty and E. Negri, “Conceptualizing The Digital Thread for Smart Manufacturing: a Systematic Literature Review,” J. Intell. Manuf., vol. 35, no. 8, pp. 3629–3653, Dec. 2024, doi: 10.1007/s10845-024-02407-1.
K. Lepenioti et al., “Machine Learning for Predictive and Prescriptive Analytics of Operational Data in Smart Manufacturing,” in Advanced Information Systems Engineering Workshops, S. Dupuy-Chessa and H. A. Proper, Eds., Cham: Springer International Publishing, 2020, pp. 5–16.
D. Wang and T. Yang, “Research on the Promotion Effect of the Marketization of Data Elements on the Digital Transformation of Manufacturing Enterprises: An Empirical Evaluation of a Multiperiod DID Model,” Sustainability, vol. 17, no. 7, p. 3199, Apr. 2025, doi: 10.3390/su17073199.
K. Salim, L. Damayanti, M. Puspita, S. Liujaya, and A. S. Girsang, “Data warehouse using Kimball approach in computer maniac,” IOP Conf. Ser. Mater. Sci. Eng., vol. 725, no. 1, p. 012099, Jan. 2020, doi: 10.1088/1757-899X/725/1/012099.
A. N. R. Batubara, M. A. R. Darus, S. R. Putri, W. Ananda, and N. Nurbaiti, “Data warehouse Model Design PT. Pos Indonesia,” Formosa Journal of Computer and Information Science, vol. 2, no. 2, pp. 129–140, Aug. 2023, doi: 10.55927/fjcis.v2i2.5042.
B. Uddin, E. M. L. Wijayadi, A. Z. Maharani, and K. W. A. Barren, “Analisis Data warehouse Pada Perpustakaan Universitas XYZ Untuk Efisiensi Manajemen Menggunakan Metode Kimball 4 Langkah,” Jurnal Informatika: Jurnal Pengembangan IT, vol. 10, no. 2, pp. 503–511, Apr. 2025, doi: 10.30591/jpit.v10i2.7323.
A. H. Amirullah and Y. Anis, “Design and Development of a Data warehouse for PT. CMS Using the Nine-Step Kimball Method,” International Journal Software Engineering and Computer Science (IJSECS), vol. 5, no. 1, pp. 141–153, Apr. 2025, doi: 10.35870/ijsecs.v5i1.3453.
V. L. Takács, K. Bubnó, G. G. Ráthonyi, É. B. Bába, and R. Szilágyi, “Data warehouse Hybrid Modeling Methodology,” Data Sci. J., vol. 19, Oct. 2020, doi: 10.5334/dsj-2020-038.
K. Rabuzin, M. Cerjan, and A. Lovrenčić, “Data warehouse Design – Star Schema Synthesis Algorithm,” TEM Journal, vol. 14, no. 2, pp. 1707–1714, May 2025, doi: 10.18421/TEM142-68.
A. Gosain and J. Singh, “Comprehensive Complexity Metric for Data warehouse Multidimensional Model Understandability,” IET Software, vol. 14, no. 3, pp. 275–282, Jun. 2020, doi: 10.1049/iet-sen.2019.0150.
R. Tardío, A. Maté, and J. Trujillo, “A New Big Data Benchmark for OLAP Cube Design Using Data Pre-Aggregation Techniques,” Applied Sciences, vol. 10, no. 23, p. 8674, Dec. 2020, doi: 10.3390/app10238674.
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